Model Card Drafterv0.1.0

A bank's data science team has built a gradient-boosted classifier to predict loan default for retail lending. Before deployment they must draft a Model Card to satisfy internal model risk management and EU AI Act high-risk documentation, and this skill guides them step by step through intended use, training data, disaggregated fairness metrics, and limitations.

A health-tech company developed a model that flags patients needing urgent review from EHR notes. Regulatory affairs and clinical governance teams use the skill to produce a Model Card covering sensitive health data, disparate impact across demographic groups, and required human-in-the-loop oversight before hospital rollout.

An enterprise MLOps platform team needs to attach Model Cards to every model version in their registry before promotion to production. The skill converts the model owner's description and evaluation results into a structured DRAFT that triggers mandatory governance sign-off and flags unresolved documentation gaps.

An HR technology vendor is adding an AI resume-ranking model to its platform and must assess high-sensitivity employment risks. A responsible-AI lead uses the skill to document out-of-scope uses, disaggregated performance by gender and geography, and fairness interventions before customer-facing release.

A government digital services agency pilots a model to prioritize benefit applications. Policy and data governance staff use the skill to produce a Model Card with disaggregated performance by income and language, privacy risk analysis, and explicit human-in-command oversight recommendations before public launch.

SaaS vendors integrate the skill into their model registry or AI governance suite so customers can generate and track Model Cards alongside model versions. It becomes a required artifact for approval workflows and audit trails.

Consultancies and regulatory-tech firms bundle the skill into advisory engagements that produce audit-ready Model Cards for high-risk AI systems. Recurring engagements combine automated drafting with expert human review and filing support.

The core skill is released open-source to drive adoption among ML engineers, while a paid tier offers team collaboration, gap tracking, reviewer sign-off workflows, and export to regulatory templates.